Automated Segmentation and Quantification of Histology Fragments for Enhanced Macroscopic Reporting

Manual tissue documentation is a critical step in the field of pathology that sets the stage for microscopic analysis and significantly influences diagnostic outcomes. In routine practice, technicians verbally dictate descriptions of specimens during gross examination; these are later transcribed into macroscopic reports. Fragment sizes are measured manually with rulers; however, these measurements are often inconsistent for small, irregular biopsies. No photographic record is captured for traceability. To address these limitations, we propose a proof-of-concept framework that automates the image capture and documentation of biopsy and resection cassettes. It integrates a custom imaging platform and a segmentation pipeline leveraging the YOLOv8 and YOLOv9 architectures to improve accuracy and efficiency. The framework was tested in a real clinical context and was evaluated on two datasets of 100 annotated images each, achieving a mask mean Average Precision (mAP) of 0.9517 ± 0107 and a tissue fragment spatial accuracy of 96.20 ± 1.37%. These results demonstrate the potential of our framework to enhance the standardization, reliability, and speed of macroscopic documentation, contributing to improved traceability and diagnostic precision.